What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?
'Backprop' is short for 'backpropagation of error' in order to avoid confusion when using backpropagation term.
Basically backpropagation refers to the method for computing the gradient of the case-wise error function with respect to the weights for a feedforward networkWerbos. And backprop refers to a training method that uses backpropagation to compute the gradient.
So we can say that a backprop network is a feedforward network trained by backpropagation.
The 'standard backprop' term is a euphemism for the generalized delta rule which is most widely used supervised training method.
Source: What is backprop? at FAQ of Usenet newsgroup comp.ai.neural-nets
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Yes, as Franck has rightly put, "backprop" means backpropogation, which is frequently used in the domain of neural networks for error optimization.
For a detailed explanation, I would point out this tutorial on the concept of backpropogation by a very good book of Michael Nielsen.
We need to compute the gradients in-order to train the deep neural networks. Deep neural network consists of many layers. Weight parameters are present between the layers. Since we need to compute the gradients of loss function for each weight, we use an algorithm called backprop. It is an abbreviation for backpropagation, which is also called as error backpropagation or reverse differentiation.
It can be understood well from the following paragraph taken from Neural Networks and Neural Language Models
For deep networks, computing the gradients for each weight is much more complex,since we are computing the derivative with respect to weight parameters that appear all the way back in the very early layers of the network, even though the loss is computed only at the very end of the network.The solution to computing this gradient is an algorithm called error backpropagation or backprop. While backprop was invented for neural networks, it turns out to be the same as a more general procedure called backward differentiation, which depends on the notion of computation graphs.